CN114019380B - Calendar life extension prediction method for battery cell - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000003860 storage Methods 0.000 claims abstract description 93
- 238000003062 neural network model Methods 0.000 claims abstract description 41
- 238000002474 experimental method Methods 0.000 claims abstract description 29
- 230000004913 activation Effects 0.000 claims description 6
- 230000014759 maintenance of location Effects 0.000 abstract description 4
- 238000013528 artificial neural network Methods 0.000 description 12
- 238000012360 testing method Methods 0.000 description 11
- 230000015556 catabolic process Effects 0.000 description 5
- 238000006731 degradation reaction Methods 0.000 description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 230000032683 aging Effects 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
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- 229910052744 lithium Inorganic materials 0.000 description 2
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention relates to a calendar life extension prediction method of an electric core, which comprises the following steps: step 1, selecting calendar experiment data of at least three groups of battery cores, wherein each group of battery cores has different storage charge amounts, and calendar storage experiments of the same group should record calendar life decay data of at least 5 times at different times; the storage charge quantity of the experimental data in the same group is the same, but the storage temperatures are different, and at least two groups contain more than 2 different storage temperatures; and 2, predicting the service lives of different storage temperatures by using a semi-empirical formula, and predicting the service lives of the storages of different storage charge amounts by using a neural network model. According to the method, the semi-empirical formula and the neural network model are adopted to predict the storage life of different SOCs, so that less experimental data can be used for predicting life decay data on any temperature, SOCs and time, and the working intensity is greatly reduced.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a calendar life extension prediction method of an electric core.
Background
The calendar life of the battery cell is an important parameter for ensuring the quality of the battery in the research and development stage, and the testing process comprises different stored charge amounts (SOC), temperatures, clamping plate pressures and the like. If the calendar life characteristics of the battery cells are to be comprehensively mastered, the above variables need to be traversed as much as possible for experiments. These tests consume a lot of manpower and material costs, but the influence relationship of many variables on the service life is regular and circulated and can be obtained by establishing a battery model.
CN106093781a discloses a calendar life test method of a power lithium ion battery. The method disclosed by the method combines the factors of the charge state, the temperature and the test data in the shelving process of the power lithium ion battery to test the calendar life of the battery. Performing linear fitting on the test result to obtain calendar lives of different rest states, and performing fitting analysis on the battery rest conditions of different states by combining actual use conditions to obtain the calendar lives of the batteries under comprehensive use conditions; the fitted curve can be represented by the formula Q (t) = atz. The estimation method disclosed by the method comprehensively considers the characteristics of the charge state, the temperature and the specific use process, so that the test result is closer to the actual use performance of the power lithium ion battery.
CN110320474a discloses a life prediction method of a lithium ion battery aging model, which comprises the following steps: (1) Testing the capacity retention rate and the value of the direct current internal resistance of the lithium battery after being stored and laid down along with the storage time under different temperatures of the lithium battery in a 50% SOC state; (2) Storing the batteries in different voltage states for the same time at the same temperature, and performing alternating current impedance test and direct current internal resistance test; (3) Analyzing the relativity of calendar life capacity attenuation, alternating current internal resistance and direct current internal resistance change with temperature and voltage, and describing the change process of experimental ageing data along with time by fitting a function; (4) And analyzing the model according to the fitted data, fitting the actually measured effective data, and effectively predicting the calendar life and the internal resistance parameters of the battery in the future. The method disclosed by the invention can infer the real life state from the accelerated aging test, is simpler, does not need high-end equipment and complex operation, and greatly shortens the test period.
After acquiring a small amount of calendar stored data, engineers want a cell model or a simple formula to deduce the calendar life decay trend of a cell under any SOC and temperature, and the previous method may approximate that the decay rules of similar SOCs and temperatures are consistent, or acquire the calendar life of a position value by using a linear interpolation method. However, the above method is too rough and the battery calendar aging process is a complex nonlinear process, and there is a need for an empirical and complex model-based method that more accurately predicts unknown calendar life.
In view of the foregoing, it is important to develop a method that facilitates small amounts of calendar storage data, and more accurately predicts unknown calendar life.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a calendar life extension prediction method of a battery cell, which can predict any life degradation number in temperature, SOC and time with less experimental data.
To achieve the purpose, the invention adopts the following technical scheme:
In a first aspect, the present invention provides a method for predicting calendar life extension of a battery cell, the method comprising the steps of:
Step 1, selecting calendar experiment data of at least three groups (such as three groups, four groups, five groups and the like) of battery cores, wherein each group of battery cores has different SOCs, and calendar storage experiments of the same group should record calendar life decay data of at least 5 times (such as 5 times, 6 times, 7 times and the like) at different times;
the same set of experimental data SOC is the same, but the storage temperatures are different, and at least two sets contain more than 2 (e.g., 3, 4, 5, etc.) different storage temperatures;
And 2, predicting the service lives of different storage temperatures by using a semi-empirical formula, and predicting the service lives of the storage of different SOCs by using a neural network model.
According to the method, the semi-empirical formula and the neural network model are adopted to predict the storage life of different SOCs, so that less experimental data can be used for predicting life decay data on any temperature, SOCs and time, and the working intensity is greatly reduced.
Preferably, in step 2, the semi-empirical formula is shown in formula i:
In the method, in the process of the invention, The calendar capacity loss rate of the battery is represented by B cal (SOC), which is a coefficient related to the stored charge, ea cal is an activation energy parameter, R is a molar gas constant, the value 8.314J/(mol.K), t is time, and z cal is a dimensionless constant.
In the present invention, the activation energy coefficient R is assumed to be 8.314J/(mol.K).
Preferably, the neural network model is a back propagation neural network model (BP).
Preferably, the number of layers of the BP neural network model is at least 3.
Preferably, the step 2 is: firstly, predicting the service lives of different temperatures by using a semi-empirical formula, and then predicting the service lives of different SOCs by using a neural network model;
Or (b)
The method comprises the steps of predicting the service lives of the storage of different SOCs by using a neural network model, and predicting the service lives of different storage temperatures by using a semi-empirical formula.
Preferably, in the step 2, predicting the life of the different storage temperatures by using a semi-empirical formula specifically includes:
Substituting each experimental data in the step 1 into a semi-empirical formula to calibrate two parameters of B cal (SOC) and Ea cal, and deducing other life decay data of the same SOC and different temperatures according to the semi-empirical formula with known parameters of B cal (SOC) and Ea cal.
Preferably, the life decay data includes a stored time, a stored temperature, and a stored SOC as inputs to the neural network model, and a calendar life as outputs from the neural network model, with life decay data at any temperature, SOC, and time being predicted.
In a second aspect, the present invention provides a method for predicting calendar life extension of a battery cell, the method comprising the steps of:
step 1, selecting calendar experiment data of at least three groups of battery cells, wherein each group of battery cells has different storage SOCs, and calendar storage experiments of the same group should record calendar life decay data of at least 5 times at different times;
the same set of experimental data storage SOCs are the same, but the storage pressures or humidities are different, and at least two sets contain more than 2 different storage pressures or humidities;
and 2, predicting the service lives of different storage pressures or humidity by using a semi-empirical formula, and predicting the service lives of the storage of different SOCs by using a neural network model.
Preferably, in step 2, the semi-empirical formula is shown in formula ii:
In the method, in the process of the invention, The calendar capacity loss rate of the battery is represented by B cal (SOC), the coefficient related to the stored charge, ea cal is an activation energy parameter, R is a molar gas constant, the value 8.314J/(mol.K), X is the storage pressure or the storage humidity, t is the time, and z cal is a dimensionless constant.
Preferably, the neural network model is a BP neural network model.
Preferably, the number of layers of the BP neural network model is at least 3.
Preferably, the step 2 is: firstly, predicting the service lives of different temperatures by using a semi-empirical formula, and then predicting the service lives of different SOCs by using a neural network model;
Or (b)
The method comprises the steps of predicting the service lives of the storages of different SOCs by using a neural network model, and predicting the service lives of the storages of different temperatures by using a semi-empirical formula.
Preferably, the step 2 is: firstly, predicting the service lives of different storage pressures or humidity by using a semi-empirical formula, and then predicting the service lives of the storage of different SOCs by using a neural network model;
Or (b)
The method comprises the steps of predicting the service lives of the storage of different SOCs by using a neural network model, and predicting the service lives of different storage pressures or humidity by using a semi-empirical formula.
Preferably, in the step 2, predicting the life of different storage pressures or humidities by using a semi-empirical formula specifically includes:
Substituting each experimental data in the step 1 into a semi-empirical formula to calibrate two parameters of B cal (SOC) and Ea cal, and deducing other life decay data of the same SOC, different storage pressures or storage humidity according to the semi-empirical formula with known parameters of B cal (SOC) and Ea cal.
Compared with the prior art, the invention has the following beneficial effects:
According to the method, the semi-empirical formula and the neural network model are adopted to predict the storage life of different SOCs, so that the life decay data on any temperature, SOCs and time can be predicted by less experimental data, the working intensity is greatly reduced, and compared with the actual experimental data, the error of a prediction result of the method is within 0.3%.
Drawings
FIG. 1 is a graph of the calendar life decay curve for a cell at 100% SOC at different temperatures for example 1;
FIG. 2 is a graph of the calendar life decay curve for a cell at 80% SOC at different temperatures for example 1;
FIG. 3 is a schematic diagram of experimental data distribution in example 1;
FIG. 4 is a schematic diagram of predicted data points for the semi-empirical formula of example 1;
FIG. 5 is a schematic diagram of predicted data points for the neural network of example 1;
FIG. 6 is a graph comparing the semi-empirical model predictions versus experimental curves of example 1;
FIG. 7 is a graph comparing model predictions of neural network to experimental curves of example 1.
Detailed Description
To facilitate understanding of the present invention, examples are set forth below. It will be apparent to those skilled in the art that the examples are merely to aid in understanding the invention and are not to be construed as a specific limitation thereof.
It is to be noted that the embodiments of the invention and the features of the embodiments may vary in form and in particular number of values without conflict.
Calendar life mentioned in embodiments of the present invention: refers to the deterioration of the life of a battery over time when the battery is stored under specific conditions.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is also noted that the terminology used in the application is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the application. The values of experimental storage SOC, values of storage temperature, number of experimental groups, number of layers of neural network, type of neural network as used herein are not strictly specified, including the upper and lower layers unless specifically noted. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or groups thereof.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
Example 1
Taking a certain type of battery cell as an example, calendar life experimental data under the condition of partial SOC and temperature combination is completed, and the calendar life degradation trend under the storage condition of 97% SOC and 60 ℃ is expected to be obtained. The existing experimental data are shown in table 1.
Table 1 calendar experimental data
The SOC and temperature distribution with experimental data were analyzed as shown in table 2, in which the vacant part was not predicted with data, and the data in fig. 3, in which the border line is a broken line, is experimental data.
Table 2 experimental data distribution table
| Temperature/SOC | 30℃ | 45℃ | 52.5℃ | 60℃ |
| 30％SOC | Experiment | |||
| 80％SOC | Experiment | Experiment | Experiment | |
| 97％SOC | Target value | |||
| 100％SOC | Experiment | Experiment |
And (3) finding out the line numbers of more than two rows of experimental data, and modeling the calendar decay of the battery cells with different temperatures of the same SOC by using a semi-empirical formula, wherein the semi-empirical formula is shown as a formula I, and the same SOC is a fixed value under the assumption that the activation energy coefficient R= 8.314. Unknown coefficients in the formula are obtained through a pending coefficient method, and a semi-empirical formula with different temperatures equal to the SOC is obtained.
100% SOC as formula 1), 80% SOC as formula 2), and corresponding calendar life decay curves as shown in FIGS. 1 and 2.
Q loss＝3.485×e^04×exp(-425.6÷8.314÷(273.15+T))×t0.5 formula 1)
Q loss＝3.023×e^04×exp(-56.16÷8.314÷(273.15+T))×t0.5 formula 2)
For the same SOC, lifetime degradation at different temperatures can be predicted by the above semi-empirical formula, and the lifetime distribution of the predicted increase condition points is shown in table 3, corresponding to which fig. 4 shows the newly added shadow slash data in fig. 4 as the predicted condition points of the increase semi-empirical formula:
table 3 increasing the semi-empirical formula prediction condition points
| Temperature/SOC | 30℃ | 45℃ | 52.5℃ | 60℃ |
| 30％SOC | Experiment | |||
| 80％SOC | Experiment | Experiment | Experiment | Semi-empirical formula |
| 97％SOC | Target value | |||
| 100％SOC | Semi-empirical formula | Experiment | Semi-empirical formula | Experiment |
After predicting the service lives of different temperatures of different SOCs in table 3 by using semi-empirical formulas 1) and 2), a series of empirical formulas still remain to predict position points, and the currently existing data service life relation needs to be learned by a neural network method, and then the influence of SOCs and temperatures on calendar service life is predicted. It should be emphasized that the semi-empirical formula can calibrate the life degradation of different SOCs according to the adjustment of B cal (SOCs) in formula I, but the relationship of SOCs to degradation cannot be predicted.
And (3) establishing a BP neural network model, wherein the layer number is 3, the input parameters are the storage days, the storage SOC and the storage temperature, and the output parameters are the calendar life of the battery cell.
TABLE 4 increasing neural network prediction Condition Point
| Temperature/SOC | 30℃ | 45℃ | 52.5℃ | 60℃ |
| 30％SOC | Neural network | Experiment | Neural network | Neural network |
| 80％SOC | Experiment | Experiment | Experiment | Semi-empirical formula |
| 97％SOC | Neural network | Neural network | Neural network | Target value |
| 100％SOC | Semi-empirical formula | Experiment | Semi-empirical formula | Experiment |
Specific life prediction values for the examples are as follows:
table 5 example life prediction data for cells
The life decay data on any temperature, SOC and time can be predicted after the neural network model is built, taking 60 ℃ and 97% SOC as examples which are proposed at the beginning of the example, the calendar life of 365 days can be predicted as shown in Table 6, and correspondingly, the data of the newly added shadow vertical lines in FIG. 5 are predicted points after the neural network model is built:
TABLE 6
In summary, the method predicts the storage life of different SOCs by using the semi-empirical formula and the neural network model, can predict life decay data on any temperature, SOCs and time with less experimental data, and greatly reduces the working strength.
After the extended predictive model is obtained, the model accuracy can be analyzed by the difference between the predictive result and the experimental data, and the error value and the comparison curve are shown in fig. 6, 7, table 7 and table 8. From the graph analysis, the method disclosed by the invention can control the prediction and experimental value comparison errors to be within 0.3% of the capacity.
TABLE 7 semi-empirical formula prediction error
TABLE 8 neural network model prediction error
Because the life formula with increased humidity and pressure is only different from the formula form, the parameter calibration and life extension prediction methods are consistent, and the description is omitted in the examples.
The applicant states that the detailed method of the present invention is illustrated by the above examples, but the present invention is not limited to the detailed method described above, i.e. it does not mean that the present invention must be practiced in dependence upon the detailed method described above. It should be apparent to those skilled in the art that any modification of the present invention, equivalent substitution of raw materials for the product of the present invention, addition of auxiliary components, selection of specific modes, etc., falls within the scope of the present invention and the scope of disclosure.
Claims (7)
1. A method for predicting calendar life extension of a battery cell, the method comprising the steps of:
Step 1, selecting calendar experiment data of at least three groups of battery cores, wherein each group of battery cores has different storage charge amounts, and calendar storage experiments of the same group should record calendar life decay data of at least 5 times at different times;
The storage charge quantity of the experimental data in the same group is the same, but the storage temperatures are different, and at least two groups contain more than 2 different storage temperatures;
step 2, predicting the service lives of different storage temperatures by using a semi-empirical formula, and predicting the storage lives of different storage charge amounts by using a neural network model;
in step 2, the semi-empirical formula is shown as formula I:
Wherein Q l c o a s l s is the calendar capacity loss rate of the battery, B cal (SOC) is a coefficient related to the stored charge, ea cal is an activation energy coefficient, R is a molar gas constant, the value is 8.314J/(mol.K), T is the storage temperature, T is time, and z cal is a dimensionless constant;
in the step 2, predicting the life of different storage temperatures by using a semi-empirical formula specifically includes:
Substituting each experimental data in the step 1 into a semi-empirical formula, calibrating two parameters of Bcal (SOC) and Eacal, and deducing life decay data of the same storage charge quantity and different storage temperatures according to the semi-empirical formula with known Bcal (SOC) and Eacal parameters;
The life decay data comprises storage time, storage temperature and storage charge quantity as inputs of a neural network model, calendar life as outputs of the neural network model, and life decay data of any temperature, storage charge quantity and time is predicted.
2. The method of claim 1, wherein the neural network model is an error counter-propagating neural network model.
3. The method of claim 2, wherein the number of layers of the error-back propagation neural network model is at least 3.
4. The method for predicting the calendar life of a battery cell according to claim 1, wherein the step 2 is: firstly, predicting the service lives of different storage temperatures by using a semi-empirical formula, and then predicting the storage lives of different storage charge amounts by using a neural network model;
Or (b)
The method comprises the steps of predicting the storage life of different storage charge amounts by using a neural network model, and predicting the life of different storage temperatures by using a semi-empirical formula.
5. A method for predicting calendar life extension of a battery cell, the method comprising the steps of:
Step 1, selecting calendar experiment data of at least three groups of battery cores, wherein each group of battery cores has different storage charge amounts, and calendar storage experiments of the same group should record calendar life decay data of at least 5 times at different times;
The same group of experimental data has the same storage charge quantity but different storage pressure or humidity, and at least two groups contain more than 2 different storage pressures or humidity;
step 2, predicting the service lives of different storage pressures or humidity by using a semi-empirical formula, and predicting the storage lives of different storage charge amounts by using a neural network model;
in step 2, the semi-empirical formula is shown as formula ii:
In the method, in the process of the invention, B cal (SOC) is a coefficient related to the stored charge, ea cal is an activation energy parameter, R is a molar gas constant, the value 8.314J/(mol.K), X is the storage pressure or the storage humidity, t is time, and z cal is a dimensionless constant;
in step 2, predicting the life of different storage pressures or humidities using a semi-empirical formula specifically includes:
Substituting each experimental data in the step 1 into a semi-empirical formula to calibrate two parameters of B cal (SOC) and Ea cal, and deducing life decay data of the same storage charge quantity and different storage pressures or storage humidity according to the semi-empirical formula with known parameters of B cal (SOC) and Ea cal;
The life decay data comprises storage time, storage temperature and storage charge quantity as inputs of a neural network model, calendar life as outputs of the neural network model, and life decay data of any storage pressure or storage humidity, storage charge quantity and time is predicted;
The neural network model is an error counter-propagating neural network model.
6. The method of claim 5, wherein the number of layers of the error-back propagation neural network model is at least 3.
7. The method for predicting the calendar life of a battery cell according to claim 5, wherein the step 2 is: firstly, predicting the service lives of different storage pressures or humidity by using a semi-empirical formula, and then predicting the storage lives of different storage charge amounts by using a neural network model;
Or (b)
The neural network model is used for predicting the storage life of different storage charge amounts, and then the semi-empirical formula is used for predicting the life of different storage pressures or humidity.
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